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Found 11 papers, 3 papers with code
Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization
Existing optimization methods often focus on finding a set of Pareto solutions with different optimal trade-offs among the objectives.
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution.
PMGDA: A Preference-based Multiple Gradient Descent Algorithm
It is desirable in many multi-objective machine learning applications, such as multi-task learning with conflicting objectives and multi-objective reinforcement learning, to find a Pareto solution that can match a given preference of a decision maker.
Multi-Objective Reinforcement Learning
Multi-Task Learning
+1
UMOEA/D: A Multiobjective Evolutionary Algorithm for Uniform Pareto Objectives based on Decomposition
Multiobjective optimization (MOO) is prevalent in numerous applications, in which a Pareto front (PF) is constructed to display optima under various preferences.
Panacea: Pareto Alignment via Preference Adaptation for LLMs
Panacea trains a single model capable of adapting online and Pareto-optimally to diverse sets of preferences without the need for further tuning.
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning.
Dealing with Structure Constraints in Evolutionary Pareto Set Learning
In this work, we make the first attempt to incorporate the structure constraints into the whole solution set by a single Pareto set model, which can be efficiently learned by a simple evolutionary stochastic optimization method.
Continuation Path Learning for Homotopy Optimization
Homotopy optimization is a traditional method to deal with a complicated optimization problem by solving a sequence of easy-to-hard surrogate subproblems.
Pareto Set Learning for Expensive Multi-Objective Optimization
This work represents the first attempt to model the Pareto set for expensive multi-objective optimization.
A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting
Electricity consumption forecasting has vital importance for the energy planning of a country.
Simplifying Polylogarithms with Machine Learning
Polylogrithmic functions, such as the logarithm or dilogarithm, satisfy a number of algebraic identities.
